Genetic Data Analysis and Database Tools

ABSTRACT

A computerized tool and method for delivery of pharmacogenetic and pharmacological information, comprising a core system having algorithms and databases for storing, collating, accessing, cross-referencing, and interpreting genetic and pharmacologic data, with a graphical user interface for a client network of providers of laboratory genetic testing services to access the core services under contract. The system includes “paypoints” in support of improved business models. Included are mechanisms for ‘pass through’ third party and insurance reimbursement for interpretive reports, insurance reimbursement for on-line access to pharmacogenetic information at the point of care, tools for market segmentation, and a conversion tool for capturing new subscribers. Also disclosed are tools and predictive algorithms for preventing drug-drug and drug-gene adverse drug reactions.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to provisional patent application Ser.No. 60/901,528 and to provisional patent application Ser. No.61/026,724, from which priority is claimed under 35 USC 119(e).

BACKGROUND

Traditionally, physicians have been expected to retain in memoryknowledge relating to potential adverse drug reactions, pharmacology,and pharmacogenetics, or to have access to such information frompublished (generally hard-copy) reports—information that is notaccessible from a single source and which is increasingly complex. Morerecently, some classes of information, for example labeling warningspublished in the PDR, have become accessible through wireless and PDAdevices. There is increasing interest in expanding the availability ofthis kind of information at the point of care.

A basic problem with all such information, however, is the need forcomputer systems, databases, networks, and software tools to display andbring to the foreground the information most relevant to the issue athand, a problem that requires extensive software development. In short,it is no longer sufficient to merely publish information in the form ofa text book or a reference manual. But because of the difficulty inobtaining reimbursement for the costs of the software services anddatabases, progress toward sustainable software innovation anddeployment has been disappointing. Thus, there is a need in the medicalarts for business models to support computerized implementation ofsystems designed to store and process metabolic, pharmacologic, andpharmacogenetic data (herein “metabolomic data”), to interpret that datain the context of patient-specific factors such as age, pregnancy,smoking and use of alcohol (herein “clinical factors”, or “patientcharacteristics”), to make available that data at the point of care,prioritized by relevance, and to provide integrated reimbursement toolsfor the costs of the equipment, database updates and maintenance. Neededare business models that support implementations of these computerizedtools.

Mental Health Connections (Lexington, Mass.) was an early entrant intocomputerized medical bioinformatics services. Their GeneMedRx service,introduced in 1995, was initially based on computerized tables forlooking up drug interactions as a function of induction or inhibition ofthe cytochrome P450s involved in their metabolism. In 2006, inpartnership with Genelex (Seattle Wash.) testing was begun on systemsfor interpretation of drug-drug and drug-gene interactions within theframework of a patient's overall medication regimen. In recent versions,GeneMedRx has grown as a database and now recognizes transporter andconjugation-linked as well as cytochrome P450-linked drug interactions.The drug interaction service now also includes a novel predictivealgorithm. These efforts have provided valuable lessons in the need forimproved business models to successfully commercialize various aspectsof bioinformatics.

Marchand (USPA 2006/0289019) describes computer systems for optimizingmedical treatment based on pharmacogenetic testing and Pareto modelling.But the disclosure is silent as to how to pay for these systems. Pickar(USPA 2003/0104453) describes computer systems for minimizing adversedrug events but is silent with respect to means to recover the costs.Hoffman (USPA 2004/0197813 and USPA 2004/0199333) describes a method fordetermining whether an atypical response to drug therapy is attributableto an error in metabolism but again does not describe a business method.Early work describing the application of computers to pharmacogeneticsis described in a 1999 paper by Evans and Relling (Science 286:487-491),a 2000 paper by Ichikawa (Internal Medicine 39:523-24), and in a patentapplication that same year by Reinhoff (US 2002/0049772). Reinhoffdescribes a computer implementation of a program on a networked computerfor analyzing polymorphisms in human populations and using thisinformation to, for example, “gauge drug responses”, but these citationsagain do not address reimbursement concerns.

Although Gill-Garrison in U.S. Pat. No. 7,054,758 describes computerpreparation and delivery of genetic reports that include “personalizeddietary advice”, the report service as commercialized (Sciona, BoulderColo.) is limited to direct marketing to consumers by the testinglaboratory under ‘fee-for-service’ arrangements and does not contemplatemethods for billing such as ‘pass-through’ reimbursement models orwholesale services to contract laboratories or clinics. Nor does theservice quantitate or extrapolate the effects of impacting substances orfactors (as practiced and defined here) on the pharmacokinetics of drugmetabolism, for preparation of reports relying instead on a simplelook-up table or tables to correlate “advice” with “risk factor” andgenetic polymorphism.

Holden (USPA 2004/0088191) addresses the issue of secure access togenetic test results over a network and the use of passwords to sharegenetic test data with third parties such as physicians. Dodds (USPA2003/0135096) again recognizes the security issues, but sees that secureaccess can be linked to payment authorization in a simplefee-for-service model with on-line authorization of credit cardpurchases. Issued U.S. Pat. No. 7,054,755 also proposes prior artfinancial service means, specifically means to purchase genetic testingkits electronically, in what is basically a shopping cart model such asmight have been assembled from the teachings of U.S. Pat. No. 5,960,411,the “one-click” patent to Amazon.com, and related arts.

However, an invitation to the customer to pay directly for preventativemedical care, for example genetic testing, has not been generallyappealing or successful. More typically, customers will habitually deferthe costs of preventative medicine. Thus, whereas Larder in U.S. Pat.No. 7,058,616 states, “The main challenge in genotyping is theinterpretation of the results” (Col 9, lines 27-28), to the contrary wehave found that the main challenge is supporting the costs of therequired servers, databases, networks and programming. A particularlypreferred model, as disclosed here, eliminates the need for mentalprocesses in operation of the system. Genetic testing services are thusstill in need of improved business models built on automated systems,business models capable of generating sufficient revenue to supporttheir development and implementation at the point of care.

SUMMARY

Significant efficiencies in patient care are anticipated fromcomputerization of medical and genetic data related to drug metabolism,herein “metabolomics”. Metabolomics includes not only drug-drug anddrug-allele interactions, but drug interactions precipitated by foods,over-the-counter medicines, herbal preparations, or clinical factorssuch as age, pregnancy, smoking, alcohol use, liver disease, and soforth.

As an example of potential medical benefits and cost savings, considerthe potential savings and reduced mortality and morbidity by preventingadverse drug reactions (ADRs) to prescribed drugs. According to the FDA,it is estimated that “there are more than 2,216,000 serious ADRs inhospitalized patients, causing over 106,000 deaths annually. If true,then ADRs are the 4th leading cause of death—ahead of pulmonary disease,diabetes, AIDS, pneumonia, accidents, and automobile deaths.” In anotherstudy, “The total cost for patients with an ADR increased an average of$2401/patient (19.86% increase), . . . . Extrapolating this finding tothe entire Medicare population resulted in $516,034,829 in costsassociated with ADRs” (Bond C A et al. 2006. Adverse Drug Reactions inUnited States Hospitals. Pharmacotherapy 26:601-608). Also to beconsidered are the cost of treatment failures resulting from ADRs.

ADRs have many causes, and one of the most important and hardest topredict, but also most preventable cause, is interactions between drugs,herbals, foods (generally, “substances”) and individual genotype.Another important class of these interactions are drug-drug interactions(DDIs).

In our invention, predictive algorithms are provided that can preventmany ADRs by issuing warnings on a graphical user interface at the pointof care before the prescription is written. Graphical user interfacesfor querying hierarchical databases are gateways for transforming rawdata into customized reports or “views” relevant in real time toweighing the risks and benefits of therapeutic options. Each graphicaluser interface (GUI) also serves as a “paypoint” for automatedmanagement of reimbursement.

The novel PK predictive algorithms disclosed here have been found to besurprisingly effective in predicting drug interactions of the typesassociated with ADRs, and hence contributes to their prevention. Our PKpredictive algorithms provide a way to make quantitative predictions ofmetabolism-based interactions among substances for which there aremetabolic data but not clinical studies. The absence of clinical studiesis a serious issue; as there are thousands of drugs and other substancesthe paired interaction of which have not been studied. Rarely, clinicalstudies report on simultaneous interactions among multiple substances.Far more data is available on the metabolism of drugs and substances,mostly in the form of pharmacokinetic (PK) data, and it is thisinformation that is used to make drug interaction predictions by thealgorithms of the present invention.

The algorithms can also runs a comparative subroutine, in which knownclinical studies of drug and substance interactions are tabulated sothat the quantitative predictions of the algorithm can be comparedagainst published results, thus validating the performance of thealgorithms. The supporting databases are frequently updated to extendthe scope and power of the PK predictive algorithms.

One such PK predictive algorithm described here is a multifactorialalgorithm capable of predicting drug-drug, drug-substance, drug-gene,substance-gene, drug-clinical factor, substance-clinical factor, andmultiple complex interactions, many of which have been associated withadverse drug interactions. While in the text there are frequentreferences to ‘drug-drug’ and ‘drug-gene’ interactions, these should beinterpreted broadly to include drug-factor, substance-factor,gene-factor, and clinical factor-factor (or “patientcharacteristic”-factor) interactions. The predictive algorithms havebeen shown to be capable of processing superimposed interactions amongmultiple factors.

Tools and methods for production, processing and delivery of metabolomicand pharmacogenetic interpretive information are also disclosed,comprising a digital, computer-implemented system having algorithms anddatabases for storing, collating, accessing, cross-referencing, andinterpreting genetic and pharmacologic data, and a network or networkswhereby contracting client laboratory providers of genetic testingservices, and other customers, can access the core host servers. TheHost System includes interactive software engines (the “MedicalMetabolomics Engine” and the “Lab Report Engine”) that support animproved business model for genetic testing, test interpretation, testreporting, and assist in prevention of ADRs.

The Host System is configured for preparation of two kinds of reports:The first report type (Type I) is used by laboratories (with access tothe Host System under contract) to report genetic test data to theircustomers. It provides a formatted test report containing patient'sgenotype, diagnosis of the resulting phenotype, and drug-geneinteraction information—detailed lists of drugs for which drugmetabolism is impacted by the phenotype, for example. It is generated bythe Host System's Lab Report Engine, but optionally may be formattedwith the logo and look of the reporting contract laboratory. The secondreport type (Type II) is generated, for example, when the patient or anauthorized user of report Type I logs onto the central platform directlyand enters added confidential medical information such as drugscurrently taken, herbal usage, certain foods in the diet, and clinicalor “patient characteristic” factors such as smoking or pregnancy. TheHost System includes highly interactive GUIs with tools to select anddisplay views of the most contextually relevant analysis of drug-drugand drug-gene metabolic interactions based on patient-specificinformation inputted by the user, who may be the patient or a healthcare provider. The Type II report is generated on the fly in response tothe patient entries, and is a fully interactive webpage with multileveldisplays, including for example: ranked warnings on possible drug orherbal interactions specific to the patient's drug regime or proposedprescription use, suggestions for alternative drugs in the sametherapeutic class, annotations with links to the medical literature,recommendations for added genetic testing, and so forth.

Both the Type I and Type II interaction reports thus use a PK predictivealgorithm. The Type I report includes a drug-gene interaction report fordrugs selected by the host system. The Type II report can includedrug-drug interaction reports, where the drugs are selected by the userbased on current medications. The Type II report is thus a personalizedtool for use in managing medications. The predictive algorithms used inthe two report types are thus modified for the purpose to which they areemployed, and can be modified further for use with other interactingfactors.

The two report types are presented on different GUIs. In the currentembodiment, the Type I report is presented by either of two GUIs builtinto the Lab Report Engine. The Type II report is presented by a GUIspecific for the Medical Metabolomics Engine, as will be explainedfurther.

Both Type I and Type II report interfaces are accessible in real time onany network, including the world wide web, including wireless telephonesand PDAs, or on an intranet or wireless intranet network. “Informationaltransaction” or “data exchange” events can be captured for billingpurposes as by credit card, subscription, direct billing, onlinedebiting, or third party billing. In this way, a long-sought need is atlast met for a system that provides flexible billing tools in support ofcovering the costs of the information technology support required forwidespread genetic testing and use of pharmacogenetic interpretiveservices.

There are differences in how the two report types are reimbursed. TheType I report, containing a genetic test result and diagnosis of aphenotype, is typically generated by the Host System at the request ofan outside laboratory accessing the Lab Report Engine under subscriptionor contract, and can be billed by the laboratory to a third party payersuch as an insurance company under “current procedural terminology (CPTcodes) codes”, or other reimbursement codes, from which the costs ofmaintaining and updating the core servers and databases can be paid tothe Host System operator. These fees can also be paid by credit carddirectly by the patient receiving the report but this has been shown notto be a preferred method. Automated fees for the Type II report areestablished for different market segments, including free trial access,monthly or yearly subscription access, “pay-per-ping” access, wholesaleaccess, and in a preferred embodiment, third party ‘pass through’billing by use of the appropriate reimbursement codes (such as when theservice is used by physicians during office visits), and so forth. Useof pass through billing, which frees the patient from the cost of theservice, also frees the physician or health care provider to makegreater use of the service.

The Type I report is automatically updated by the predictive algorithmeach time it is accessed on-line. In an improved reimbursement model,the Type I report contains interactive links and security access codesso that the recipient can access the Type II report service, thusenabling the Host Operator to convert client laboratory customers todirect-service customers.

A “sponsored-use hyperlink” embedded in a Type I report in the abovemethod has the property that when securely accessed by entering thepatient's identifiers and access code from a remote terminal, such as ata doctor's office or a home, a Type II report is created that includesupdated metabolomic content from Host System databases and offers aseries of interactive options. The Type II report is presented by a GUIdedicated for this purpose. Here, the patient may enter personalinformation such as current prescription drug usage, relevant clinicalfactors such as pregnancy, age, history of smoking, and so forth.Displayed in the resulting Type II report are detailed DDI (“drug-druginteraction”) and ADR warnings specific to the patient's personal drugregime and personal genetic data at that moment in time. In other words,the Type II report, given a phenotype and a personal drug regimen, canpredict both drug-drug and drug-gene interactions of possible immediateconcern to the user. A user may modify the drug regimen to remove theculprit drug or drugs responsible for the warning and generate anupdated report. In this way it is possible to check a prescription forpotential DDIs or ADRs before it is written. This report is updated onthe fly whenever the patient, or anyone the patient authorizes to accessthe records (e.g. a physician) accesses the Host System if the coredatabases have been updated with relevant new clinical information or ifthere is a change in the patient's medical regimen or status. Theservices provided by this GUI are billable in several ways: as a“pay-per-ping” fee to the patient or to the physician, as a subscriptionservice to the patient or to the physician, as a free trial, wholesaleto a clinic, or to third parties through the mechanism of a CPT code orother reimbursement code arranged for third party billing, and so forth.This innovative service also will function in a single-payor insurancemodel.

Whereas the initial laboratory report (Type I) can be billed by theclient laboratory to the end use customer (e.g. patient or physician) orto insurance, the second report (Type II) is configured as a directtransaction with the Host Server, and thus results in fees directlypayable by the customer to the operator of the Host System. By includingin the Type I report a link to the Type II user interface, the firstreport thus generates what is essentially a business referral to theHost System. This is beneficial to all parties because it allows thepatient or health care provider to better manage ADRs, and encouragesuse of genetic testing. The Type II user interface may also displayrecommendations for further genetic testing services if indicated, orlinks to related services such as paternity testing services, and thusbecomes a central hub in a network for accessing a broad range ofmedical and genetic services or information.

BRIEF DESCRIPTION OF THE DRAWINGS

The teachings of the present invention can be readily understood byconsidering the following detailed description in conjunction with theaccompanying drawings, in which:

FIG. 1 is a schematic of a computerized apparatus for storing,collating, accessing, crossreferencing and interpreting metabolomicdata, with multiple GUIs capable of supporting a segmented businessmodel.

FIG. 2 is a flow diagram illustrating the operation of Paypoint 1.

FIG. 3 is a flow diagram illustrating the operation of Paypoint 2.

FIG. 4 is a flow diagram illustrating the operation of Paypoints 3, 4and 5.

FIG. 5 is a flow diagram outlining the major steps of a PK predictivealgorithm and showing subroutines A and B.

FIG. 6 is a detail showing the steps of subroutine A.

FIG. 7 is a detail showing the steps of subroutine B.

FIG. 8 is an example of a table used by a computer algorithm for makingcalculations of the effect of interacting factors on the AUC of a drug.

FIG. 9 is an example of an experimental Type I lab report containing a“sponsored-user” hyperlink.

FIG. 10 is a detail of an interactive webpage for entering personalinformation and current drug regimen.

FIG. 11 is a detail of an interactive webpage containing a commandinterface for preparing a Type II report.

FIG. 12 is a detail of an interactive Type II report showing a DDI in a2D6 intermediate metabolizer.

FIG. 13 is a detail of an interactive screen demonstrating acomputerized tool for use of market segmentation in a business plan forvending genetic information and interpretation services.

DETAILED DESCRIPTION

Although the following detailed description contains specific detailsfor the purposes of illustration, one of ordinary skill in the art willappreciate that many variations and alterations to the following detailsare within the scope of the invention as claimed. Accordingly, theexemplary embodiments of the invention described below are set forthwithout any loss of generality to, and without imposing limitationsupon, the claimed invention.

Adverse Drug Reaction (ADR): as used here, describes a response to adrug or substance which is noxious and unintended, and which occurs atdoses normally used in man for prophylaxis, diagnosis, or therapy ofdisease, or for modification of a physiological function. ADRs have manycauses and one the most important and hardest for clinicians to predict,but often most preventable cause, is interactions among drugs, herbals,foods and genetic factors. At the molecular level, a comprehensivetreatment includes both Phase 1 and Phase 2 metabolic systems, includingconjugation enzymes (such as uridine diphosphglucuronylsyltransferasesand sulfotransferases), transporters (such as ABC and SLCO), as well asthe known cytochrome P450 oxidative enzymes.

Phase I reactions can occur by oxidation, reduction, hydrolysis,cyclization, and decyclization reactions. Oxidation involves theenzymatic addition of oxygen or removal of hydrogen, carried out bymixed function oxidases, often in the liver. These oxidative reactionstypically involve a cytochrome P450 haemoprotein, NADPH and oxygen. Ifthe metabolites of phase I reactions are sufficiently polar, they may bereadily excreted at this point. However, many phase I products are noteliminated rapidly and undergo a subsequent “Phase II” reaction in whichan endogenous substrate combines with the newly incorporated functionalgroup to form a highly polar conjugate.

Phase II reactions (e.g., glucuronidation, sulfonation,glutathionyl-conjugation or amino acid conjugation) speed clearance byincreasing polarity, and involve conjugation at functional groups formedin Phase I metabolism.

Information about genetic loci responsible for polymorphisms of Phase Iand Phase II enzymes and associated transporters is thus of significancein making predictions about potential ADRs. Drug-drug;substance-substance; drug-gene; substance-gene, and more generally“substance-factor” interactions must be considered in assessing possibleADRs.

Drug-Drug Interactions (DDI): include interactions between pairs ofdrugs or substances and among multiple drugs or substances that resultin changes in the pharmacokinetic parameters of one of the interactingdrugs or substances. Drug-drug interactions (DDIs) are not ADRs but manyADRs are caused by DDIs. We note that some drug interactions arebeneficial because the inhibition of metabolism of a drug can increasethe patient's exposure to it and increases the therapeutic benefit.Among such interactions, the cytochrome P450 system, which plays a majorrole in metabolizing drugs and other potentially toxic substances, isbelieved responsible for about 70% of DDIs.

Drug: is a substance used as a medicine.

Prodrug: is a drug which is not therapeutically active until itundergoes metabolism in the body.

Bioactive: a substance with biological activity. Bioactive is a broadterm encompassing drugs, foods, herbals, and so forth. More specificexamples not named elsewhere include lipids, surfactants, retinoids andflavonoids. Also included are metabolites of foodstuffs, drugs andsubstances.

Food: a substance ingested for flavor or nutrition.

Herbal: a plant or plant derived substance used for its therapeuticproperties.

Substance: a drug, bioactive, excipient, herbal, food, or otherchemical. As used herein, a drug-drug interaction can refer to asubstance-substance interaction. More generally, a substance-factorinteraction refers to a substance-substance, substance-gene, orsubstance-clinical factor interaction.

Factor: includes substances, bioactives, phenotypes, and “patientcharacteristics”, also termed “clinical factors”, which may cause or besubject to an interaction. Importantly, in the algorithms, factors arespecific to and associated with a patient identifier. Drug classmembership (below) is also a factor.

“Clinical factor” or “patient characteristic”: as used here, includesany patient-specific characteristic, classification or status, such aspregnancy, age, race, history of smoking or alcohol use, liverpathology, kidney pathology, cholecystectomy, colostomy, diabetes,lifestyle, and so forth, that can cause or exacerbate a substanceinteraction, DDI, or ADR. Clinical factors are generally drawn from theclinical history. The list of factors is expansible within the tablestructure of a database and can be used in an algorithm to predictpotential DDIs and ADRs, as taught here.

Drug “class membership”: as used here is also a “factor” in the contextof the PK predictive algorithm and is directed to a “synthetic” class orgroup comprised of drugs with a common side effect. Many SSRI's forexample share common side effects. Certain other drugs commonly causeQT/QTc prolongation, slowing the heart rate, such as risperidone andhaloperidol. Simultaneous administration of two such drugs can lead toadditive side effects, a DDI that is not directly linked to a geneticpolymorphism (although it would be exacerbated by one), but is picked upand displayed by the Type II PK predictive algorithm.

“Medical metabolomics”, or “metabolomics” as used here, includeselements of pharmacogenetics, pharmacology, naturopathy, biochemistry,physiology and medicine. We limit the scope of the information, strictlyfor the sake of relevance, to the patterns of metabolism of drugs andother bioactives jointly “substances”) and their interaction withmetabolic enzymes and transporters and with each other in the complexenvironment of the human body. Of particular interest is the question ofthe effect of these interactions on the pharmacokinetics, efficacy andsafety of a particular drug in a particular patient. Thus metabolomicsincludes the study of ADRs.

Bioinformatics: Bioinformatics derives knowledge from computer analysisof biological data. These data can consist of the information stored inthe genetic code, but also include experimental results from varioussources, patient statistics, and scientific literature. Research inbioinformatics methods and apparatuses is ongoing, and includesalgorithm development for storage, retrieval, and analysis of the data.

Pharmacogenetics: Refers to the evaluation of individual geneticvariation in relation to the delivery, safety, and effectiveness ofdrugs. Knowledge of individual genotypes and phenotypes makes itpossible to customize drug delivery regimens for specific patients so asto avoid ADRs and maximize the benefits of drug therapies. Additionally,pharmacogenetics encompasses the study of the differences amongindividuals with respect to gene-linked responses to a drug. Relevantlaboratory workups include “genotyping” or “genetic testing” by methodssuch as array hybridization, PCR-direct sequencing, PCR-linkedelectrophoretic restriction fragment polymorphism, or PCR-linked allelespecific primer extension. Samples include buccal swabs and bloodcollected by venipuncture or lancet. Genetic markers of interest inpharmacogenetics include polymorphisms at selected allelic loci,particularly SNPs and deletions, often referred to in the literature as“haplotypes” or “star data”. More recently, through direct sequencing ofwhole human genomes, unpaired chromosomal fragments (ie. unpairedalleles) have been discovered in which an individual is mono-allelic,and these too may also have a bearing on health and disease. Relevantpharmacogenetic information includes the testing data, sample andtesting protocols, and annotations of the primers or sequence data usedfor identification of a genotype from, all generally recognized asnecessary components of a genetic testing report. Interpretation ofpharmacogenetic data commonly requires categorization of the genotypeinto one or several phenotype classifications, for example slow or poormetabolizer, intermediate metabolizer, normal, and ultra-fastmetabolizing forms, as well as various classifications based on theorgan in which the gene is expressed.

AUC: also termed “area under the curve”, is a measure of the amount of adrug or substance the body is exposed to. It reflects both thetime-course and concentration of a drug or substance in bodily fluids.

Inhibitor: generally a ligand interacting with an enzyme, transport,conjugation, allosteric or other binding site and resulting in reducedthroughput of the substrate. Inhibition at the level of gene expressionis also contemplated by this term. In the PK prediction algorithm,published Ki values are used to estimate the interaction intensity indexIP. When a Ki is unknown, a default value is assigned. If the impactingfactor responsible for reduced throughput is a phenotype or clinicalfactor, an index value is assigned based on intensity ratings extractedfrom the research literature.

Inducer: generally a ligand causing increased expression of a generesponsible for synthesis of an enzyme or transporter. Inducer-specificexperimental and clinical ratings of induction intensity are assigned anindex value INTX comparable to the points assigned for inhibition but ofnegative sign. The intensity of inducer effects can be estimated, forexample, by Neil's rules of thumb. If the impacting factor responsiblefor increased throughput is a phenotype or clinical factor, an indexvalue is assigned based on intensity ratings in the research literature.

Interaction pair: refers to substances or factors that interact byincreasing or decreasing the metabolism of one member of the pair viaone or more metabolic pathways. One of the members of the pair is asubstrate of the pathway or pathways and is called the “victim”, theother substance or factor is called the “culprit” and can be i) aninducer, ii) an inhibitor, iii) a genetic polymorphism of a geneencoding a protein of the pathway, or iv) other clinical factor such aspregnancy or age. Inducers and inhibitors are not limited toprescription drugs, but may include clinical factors such as exposure totobacco smoke or environmental chemicals. While victims and substratesinteract via metabolic routes, it should be understood that theinteraction may involve oxidative enzymes, conjugative enzymes, ortransporters, of which cytochrome P450, glucuronyl transferase, andP-glycoprotein are illustrative examples, and that metabolism may beinfluenced by factors such as age and pregnancy, as well as geneticpolymorphism, allosteric and competitive inhibition, and induction ofgene expression. Thus “interaction pair” is not limited to drug-druginteraction.

“Victim”: is a term of art referring to a member of a drug interactionpair for which metabolic throughput is impacted, resulting in a changein AUC, ie. a change in peak or cumulative exposure.

“Culprit”: is a term of art referring to a member of a drug-drug ordrug-factor interaction pair responsible for a change in AUC, ie. inpeak or cumulative exposure, of a “victim”. Because clinical factors canalso impact drug exposure, the term “culprit” is used in a broad sense,conveying a list not only of drugs and substances, but also of clinicalfactors and patient characteristics that can impact a victim drug orsubstance. Culprits may be inhibitors or inducers.

Intensity: refers to the degree of interaction between two substances orfactors. In subroutine A of the PK predictive algorithm, the intensityof interaction is quantitated by “INTX”. Intensity indices are drawnfrom literature values and include indices of induction and inhibition.

Proportion: refers to the relative fraction R_(1/1-n) of a drug orsubstance's metabolism directed through a particular metabolic pathwaywhere more than one pathway operates in parallel.

Paypoint: an automated tool for flagging, configuring and routinginformation about a data exchange between the Host System and a user,and initiating an automated financial transaction on a billing serverthat generates a debit on an account.

Graphical user interface (GUI): a combination of one or more visual,acoustic and tactile means for engaging a computer, commonly based on amixture of graphics and text that is used to query a database. GUIsinclude tools such as keyboards and mouse pointers for enteringinformation in a computer.

Genotype: As used here refers to a genetic marker or “allele”—one ofseveral possible hereditable DNA sequences characterizing one geneticlocus of an individual. It pertains to a specific gene, but “genotype”may also be used to describe a collection of genotypes for each of a setof genes of an individual.

Phenotype: By contrast, phenotype refers to the manifestation ofexpressed genetic information, and thus indicates not only a particularprotein or set of proteins of an organism or tissue, but also variantsin the way protein expression or activity responds to environmentalfactors.

The following are representative genetic testing data. These includegenetic loci that are known to be important in drug metabolism. Alsorelevant are the disclosures of U.S. Pat. No. 7,054,758, assigned toSciona Ltd, and US Patent Application 2006/0289019, assigned to IPPMHolding SA, hereby incorporated in full by reference. Also relevant arethe disclosures of Tomalik-Scharte (Tomalik-Sharte, D et al. 2008, Theclinical role of genetic polymorphisms in drug-metabolizing enzymes.Pharmacogenetics J 8:4-15), hereby incorporated in full by reference.

CYP2D6 (cytochrome P450 2D6) is the best studied of the DMEs and acts onone-fourth of all prescription drugs, including the selective serotoninreuptake inhibitors (SSRI), tricylic antidepressants (TCA), betablockerssuch as Inderal and the Type 1A antiarrhythmics. Approximately 10% ofthe population has a slow acting form of this enzyme and 7% a super-fastacting form. Thirty-five percent are carriers of a non-functional 2D6allele, especially elevating the risk of ADRs when these individuals aretaking multiple drugs. Drugs that CYP2D6 metabolizes include Prozac,Zoloft, Paxil, Effexor, hydrocodone, amitriptyline, Claritin,cyclobenzaprine, Haldol, metoprolol, Rythmol, Tagamet, tamoxifen, andthe over-the-counter diphenylhydramine drugs, Allegra, Dytuss, andTusstat. CYP2D6 is responsible for activating the pro-drug codeine intoits active form and the drug is therefore relatively inactive in CYP2D6slow metabolizers.

CYP2C9 (cytochrome P450 2C9) is the primary route of metabolism forCoumadin (warfarin). Approximately 35% of the population are carriers ofat least one allele for the slow-metabolizing form of CYP2C9 and may betreatable with 50% or less of the dose at which normal metabolizers aretreated. Other drugs metabolized by CYP2C9 include Amaryl, isoniazid,ibuprofen, amitriptyline, Dilantin, Hyzaar, THC (tetrahydro-cannabinol),naproxen, and Viagra.

CYP2C19 (cytochrome P450 2C19) is associated with the metabolism ofcarisoprodol, diazepam, Dilantin, and Prevacid.

CYP1A2 (cytochrome P450 1A2) is associated with the metabolism ofamitriptyline, olanzapine, haloperidol, duloxetine, propranolol,theophylline, caffeine, diazepam, chlordiazepoxide, estrogens,tamoxifen, and cyclobenzaprine.

NAT2 (N-acetyltransferase 2) is a second-step DME that acts onisoniazid, procainamide, and Azulfidine. The frequency of the NAT2 “slowacetylator” in various worldwide populations ranges from 10% to morethan 90%.

VKOR vitamin K 2,3-epoxide reductase. Factor V Leiden and Factor II(Thrombin) are related to the 2C9/VKOR package in that the individual'sgenotype at this locus is a factor in predicting clotting risk.

Other genetic loci of known interest include C734A4, C734A5, C734A7,MTHFR genotype, methonine tetrahydrofolate reductase, Homocysteinemetabolism, TPMT poor metabolizer, UGT1A1, Glucuronosyl transferase(active in metabolism of Labetalol, Morphine and Naloxone),S-methyltransferase, Factor II Thrombin, Celiac Disease Panel, Factor V,obesity-associated genetic loci, and ABCB1-P-glycoprotein. Some of theseare experimental, some of proven impact on health care decisions. Whilethe application is not limited narrowly to pharmacogenetic data, and maycomprise genetic and metabolomic data more generally, the core GeneMedRxapplication server and programming is currently configured with apharmacogenetic and pharmacological database as a preferred embodiment.

The issues involved in interpreting genetic test reports and potentialdrug interactions are by no means simple. Some cytochrome P450s areexpressed in multiple tissues (e.g., CYP3A4 has intestinal and hepaticsub-routes). A drug may be metabolized by one or both of the sub-routes.Drugs may also inhibit one sub-route preferentially. Selective entryinto the brain is also controlled by independently expressed drugportals and metabolic enzymes of the blood brain barrier.

The particular tissue-specific sub-route by which a drug is metabolizedis often not known because the data were collected before sub-routeswere recognized. In the current algorithm, subroute information isutilized in predicting interactions; however, if no subroute informationis available, one embodiment of the algorithm makes a conservativeassumption that the drug is a substrate or inhibitor of all subroutes.

Given the complexity of the interactions between genotype, drugindications and other factors in delivery of personalized medicine, itshould now be clear that a computerized tool of the kind disclosed hereis essential for managing the required and associated medicalinformation.

Turning now to the figures, FIG. 1 is an overview of the host softwareengines and servers 20, which are typically under control of a singleoperating entity, the Host System operator, who is responsible forconstructing and maintaining the servers, databases, software andnetwork interfaces. The Host System operator is reimbursed as shown inthe figure, which includes five paypoints where financial transactionsmay be initiated and configured. These are paypoints 1, 2, 3, 4 and 5 asshown, and are directed to multiple market segments. Also shown arecustomer types 6 (full-use subscribers), 7 (fee-for-service users), 8(conversion subscribers), 9 (sponsored users), 10 (contractlaboratories), although these terms should be construed sensu lato andare not narrowly limited. For example, full-use subscribers may includewholesale users, and sponsored users may include promotional users.Certain customer types are interconvertible or overlapping, as will bedescribed below.

Between horizontal lines 27 and 28, the host software is responsible forinitiating the financial transactions and includes all HostSystem-compatible interfaces. Below horizontal line 28, outside softwarevendors may supply the required software, servers and user interfaces.Laboratory Billing System 25 and 3d Party Payor System 26 may also besupplied and operated by outside parties. The titleblock “3rd partypayor” refers generically to private insurance carriers, grantingagencies, government agencies and the like, where the financialtransaction is indirect and might not involve the host software orservers directly. Although depicted once, the client laboratory block 25and 3d Party Payor block 26 are indicative of a plurality of suchentities.

Computing equipment of the Host System 20 comprises the MetabolomicsEngine 21, datapipe (arrow) 29, the Genetic and Pharmacologic DatabaseEditor module 17, Administrative server 18, datapipes 31, 32, 34, 35,36, 41 and 51, and GUIs 30, 40 and 50, each of which will be explainedsubsequently in more detail. Datapipes are arrows indicating the flow ofdata in the system. Provision of hardware for computerizedimplementation of the system falls within conventional skills.

Integration of Host System functions may rely on hardwiredinterconnections or on networked interconnectability. Networkaccessibility 12 is indicated in FIG. 1 and may comprise internet,intranet, wireless networking, and so forth. Web servers, wirelessprotocols, and GUIs suitable for connectivity of these sorts are wellknown in the art. Servers 17, 18, 25 and 26 may be remote servers andwirelessly connected to the Host System, which may be a single,integrated whole or can be distributed over multiple locations. Secureaccess, digital certificates, and encrypted web pages are known in theart. Recent computer security innovations such as reCaptcha, an opensource project of Carnegie Mellon University, are also useful inimplementing secure access.

The Metabolomics engine 21 comprises databases and logic modulesprogrammed with software algorithms While the following descriptionincludes particulars, it should be understood that the number ofdatabases, the number of servers, and the location of data storagefunctions and logic modules, and so forth, may be modified by thoseskilled in the art while still consistent with the spirit and teachingsof the invention. Database 22, encoded on a computer readable medium, istermed the Genetic and Pharmacologic Database (or “Pharmacogenomicsdatabase”) and database 23, encoded on a computer readable medium, isthe Administrative Records & Clinical Records Database, also termedsimply the “Administrative Database”. The Administrative Editor 18handles business records, which are stored in Administrative Database23, and related backoffice functions such as insurance claimsprocessing, and validation and error tracing, and includes a userinterface for authorized personnel. Database 23 is also the site forsecure storage of patient medical records. Genetic and PharmacologicDatabase Editor 17 is used to update biological, pharmacological, andpharmacogenetic look-up tables in the Pharmacogenomics database 22, andincludes a user interface generally restricted to clinical specialistsresponsible for researching, maintaining and updating data extractedfrom current medical literature. The records entered in database 22 caninclude for example inhibition constants (Ki), inducers, metabolicpathways, sub-pathways, organ-specific pathways, interaction intensities(INTX), metabolic enzymes, correlations between markers for geneticpolymorphisms (such as SNPs) and phenotypes, extensive references andannotations from the medical literature, related hyperlinks, druggeneric and brand names, drugs, prodrugs, herbals, excipients,metabolite identifiers, drug interaction classifiers, drug metabolicroutes, drug metabolic route weightings (R_(1/1-n)), uptaketransporters, uptake transporter-substance interactions, organ-specifictransporters, herbal interactions with drugs, patient characteristics,patient characteristic interactions with drugs and herbals, phenotypeinteractions with drugs and herbals, phenotype interactions with patientcharacteristic factors, drug therapeutic classes, therapeuticsubstitutes, composition of pharmaceutical mixtures, clinical statusfactors, drug label warnings, warnings from the medical literature,clinical trials, and cross-references, for example, and may be expandedto include new tabulations of data as deemed useful. Phenotypes includefor example, “poor metabolizer”, “normal metabolizer”, “intermediatemetabolizer”, “ultra-metabolizer”, “reduced heterozygous expresser”, andso forth. Metabolic enzymes include for example cytochrome P450 enzymesCYP1A2, CYP2C19, CYP2C9, CYPNAT2 and CYP3A4 concerned with drugmetabolism and p-glycoprotein transporters concerned with drug uptakeand elimination. Among the algorithms programmed in the Metabolomicsengine 21 are one or more PK predictive algorithms, which calculate theimpact of phenotypic interaction, drug interaction, and clinical factorinteraction on the AUC of pharmaceutically active compounds andmetabolites, and makes predictive warnings if an adverse druginteraction is possible. Also included in the Metabolomics engine is agenotype-phenotype translator. These algorithms will be discussed inmore detail in a subsequent section.

Lab Report Engine 24 includes programming for data entry functions, forinterfacing with the databases of the Metabolomics Engine 21, and forassembling and transmitting laboratory reports. The contract laboratoryGUI 30 is used for data entry and for controlling the production of TypeI lab reports by the Host System. These reports, shown here astransmitted to user interfaces 30 and 40 via a network connection,contain laboratory-proprietary formatting and information such as a logoand contact information, patient information, and also genetic testingdata. An algorithm in the Medical Metabolomics Engine 21 interprets or“correlates” the genetic test result with a phenotype and the Lab ReportEngine incorporates the phenotype data into the Type I lab report, alsostoring a copy of the patient's record in Clinical Records database 23.Optionally included in the report is predictive content highlightinginteractions of the reported phenotype with selected drugs where thereis a likely interaction. The report takes into account that the patientmay have multiple abnormal phenotypes. Each time it is accessed, thisreport is newly created by the system (using the Type I predictivealgorithm) and reflect the most current patient genetic information andmetabolic information in the databases. Access to the report istypically password protected. The lab report also may contain a live,sponsored-use hyperlink 43 (*) which will be discussed in more detail inthe context of the paypoints.

Returning to a discussion of the paypoints and the multiple businessmodels described in FIG. 1, we turn first to paypoints 3, 4 and 5. Thesefinancial transaction initiation points are linked to data transfersindicated by datapipe arrows 32, 31 and 33 respectively. The Host Systemoperator is paid (Paypoint 4) by contracting laboratory 10, typically aspart of a subscription for access to the Host System, for a servicecomprising the delivery of an enhanced laboratory report to the end userat user interfaces 30 and 40, or optionally by displaying the webpagereport through a webserver on the laboratory's server 25. Note that theuser is a customer for genetic testing services by the contractinglaboratory and the contracting laboratory is in turn a customer of theHost System operator. The enhanced genetic test report includes adetermination of a phenotype, where the phenotype is determined by acomputerized interpretation of the raw genetic test data entered by thelaboratory. The interpretation is made by the Lab Report Engine 24 usinggenetic and bioinformatic records on the Pharmacogenomics database 22.The Host System provides subscription services to multiple contractlaboratories, but each test report is customized with the logo andcontact information of the particular laboratory providing the testing,with such files and formatting as are required by the report-generatingalgorithm being stored in Administrative database 23.

The contracting laboratory is responsible for providing the results of agenetic test on a sample submitted by a patient, health care provider,or other party. To initiate a report of the test result, contractinglaboratory 10 is provided with GUI 30 for accessing the Host System andenters patient identifier data and genetic testing result data into theHost System through datapipe arrow 31. This data is stored in the secureClinical Records Database 23. Lab Report Engine 24 builds the report andtransmits it to user interface 40, as represented by datapipe arrow 32.GUI 30 can also be used to print out a paper copy of the report formailing to the customer or for reviewing and archiving the content. Inanother embodiment, the user can obtain this report by logging on to thelaboratory server 25 and requesting it, the background operations of theHost System being seamlessly integrated into the foreground operationsof the laboratory server.

The interpretation made by the Metabolomics Engine between the genetictest result and a metabolic phenotype is a service that can be billed asan interpretive or diagnostic laboratory service under a recognized CPTcode (“Current Procedural Terminology code”) or equivalent authoritywhen performed under the supervision of a pathologist or recognizedmedical practitioner associated with the contracting laboratory. TheType I report also includes a detailed table showing the commonlyprescribed drugs available where the user resides that interactsignificantly with the phenotype. This service is also billable as partof the Lab Report and reimbursement for access to the host systemresources and predictive algorithm is passed on to the Host Systemoperator.

In one preferred embodiment of the business model, upon delivery of theenhanced “interpretive” report to user interfaces 30 and 40, thelaboratory server sets a flag that in turn results in an invoice beingsent to a third party payer 26 (Paypoint 5), such as an insurancecarrier, as indicated by datapipe arrow 33. All necessary informationthat the insurance company typically needs to process a claim will beincluded with the invoice. In this model, Paypoint 5 is external to theoperation of the Host System, but is an inducement to use the HostSystem resources and serves as a supplemental profit source for thecontracting laboratory. Contract genetic testing laboratories offeringthe enhanced power of the Metabolomics Engine have a marketableadvantage because of greatly enhanced information they can report totheir customers, information that would be very costly for any singlelaboratory to assemble, maintain, and deliver, even if the software hadbeen commercially available. Insurance carriers offering coverage forgenetic testing services, which can directly reduce medical costs byavoidance of adverse drug reactions, also have a marketable advantage.Use of existing CPT codes for automated interpretive services as a toolfor billing for enhanced genetic test reports is a novel solution to alongstanding and unmet need in the industry. Prior art models includeshopping cart-type fee-for-service billing and subscription billing.Here however, the billable event that drives the model is the fee forprofessional interpretation of the phenotype or the fee for professionalpredictive interpretation of potential drug-drug or drug-geneinteractions, and by automating the billing, a broad range ofpharmacogenetic interpretive services can be supported. Although thebillable service accrues to the contract laboratory, and is paid by athird party payor, the revenue drives the subscription fees collected bythe Host System operator to maintain the system.

Note that the end consumer is a customer of the contract laboratory andmay be a patient or a health care professional, such as a physician.Patients in many states are authorized to order laboratory testingservices in propria persona. Thus payment for interpretive services maybe made in two ways, as distinguished by Paypoints 3 and 5. Either thecontract laboratory is paid at Paypoint 3 directly by the customer fordelivering the genetic test report or by an insurer indirectly atPaypoint 5. Serendipitously, this model allows health care providers,who wish to order genetic testing, to ‘pass through’ the costs of thattesting and professional interpretation to insurance carriers (Paypoint5). In this preferred model, the patient, health care provider, orend-use customer are not parties to the resulting financial transactionsand are termed, “sponsored users” (9), who can access the report atinterface 40. The laboratory may thus offer the enhanced report serviceat interface 40 to the patient or to an authorized health care providerat no charge under this model, a surprising and unanticipated solutionto the problem of reimbursement for genetic testing services.

In the preferred arrangement, where the payor is a third-party and theinvoice is submitted with a recognized CPT code, the market for genetictesting services is shown to increase over the direct fee-for-servicemodel, and increased use results in reductions in overall costs ofhealth care delivery and increased efficiencies. The result is avirtuous cycle. The rising cost of health care delivery, which includesan important component representing the increasing frequency andseverity of adverse drug reactions and related complications andlitigation, has been well established. Access to pharmacogenetics at thepoint of care is needed to help bring this escalating cost undercontrol, but has been impeded by difficulties in discovering models andformulae for reimbursement. The problem of more widely providing genetictesting is solved by the reimbursement mechanisms described here—athird-party private insurance payor or a single-payer system is invoicedfor those costs under accepted medical billing codes and accountingpractices, allowing the health care provider to pass through those feesand access the data without an intervening fee-for-service, shoppingcart, or subscriber transaction.

Paypoint 1 provides a parallel or alternative reimbursement pathway andillustrates a second aspect of the invention whereby real time, point ofcare access to bioinformatics can be funded. A full-service subscriber 6interfaces with the Medical Metabolomics Engine 21 through Paypoint 1,as indicated by datapipe arrow 51 and GUI 50. Paypoint 1 can beconfigured for subscription access for full service customers 6, forexample with annual or semi-annual dues, but also as a “pay-per-ping”fee for access. Paypoint 1 may also be marketed and priced for wholesaleusers, for example medical clinics with multiple patients and broadbandaccess routed directly into each examination room. In this model, thepatient uses passwords or access codes to control access, but ahealthcare provider or physician can with the patient's consent, for alimited time, view the patient's genetic information on interactive userinterface 50. This interface differs from interface 40 and 30 in that itallows the user to enter and model the pharmacogenetic consequences ofvarious prescription and patient factors on a secure linkage, amongother options and services, and to store the prescription data on-line.Full service user access includes guided assistance in evaluating theclinical effect of genetic polymorphisms, aid in assessing the impact ofpatient characteristics and factors such as pregnancy, alcohol,recreational drug use and smoking in the context of the patient'sgenetic makeup, predictive warnings about probable drug interactions notreported in the medical literature (based on the novel PK predictivealgorithm), contextually specific assistance in choosing alternate drugsin a therapeutic category, annotated in-depth literature citations andhyperlinks, and ready reference to labeling, indications, packagewarnings, toxicology, and chemical information about drugs, herbals,pharmaceutical formulations, and mixtures, all in the context of currentinformation about the patient's prescription regimen. The interactiveType II report capacity is novel in that it is generated “on the fly”(by the predictive algorithm) whenever accessed and thus increases therange of its predictions whenever PK or factor data is added to thedatabase. The pharmacogenetic database is updated regularly by Editorfunction 17 so that the Type II report will always contain timelyresearch findings. The Type II report and will flag any newly discoveredor predicted interactions of immediate relevance to the particularpatient's care. That is, an interactive report accessed in lateSeptember will likely contain new information not available in earlyJune; an interactive report accessed while the patient is receiving onedrug will contain unique information not included if the patient isswitched to another drug; an interactive report accessed after theresults of a genetic test are entered will contain a whole range of newinformation not available before the test result was entered, and soforth. Thus the report is a living, dynamic view of the most relevantpatient-specific pharmacogenetic information at any given time and isaccessible at the point-of-care by those with wireless devices or withan internet connection. This underlines the importance of designingreimbursement into the system.

Another novel feature of GUI 50 is access to a PK predictive algorithmin the Medical Metabolomics Engine 21. The PK predictive algorithms,unlike prior art efforts to present drug interaction data, are designedto identify drug-drug interactions, including interactions among threeor more drugs, and to display the clinically significant interactions.The display integrates interaction studies from the clinical literatureand the predictions of the PK predictive algorithm. The PK predictivealgorithm also is designed to handle multiple polymorphism interactions,so that the significance of multiple alleles and multiple phenotypes isfully reflected in the predictions. As a novel and unexpected solutionto a longstanding problem, this algorithm is effective even in theabsence of clinical reports of a specific interaction, although whenboth a prediction and clinical study are available, priority is given tothe medical literature in the choice of warnings displayed. Thecalculation is a mixed semi-quantitative and empirical estimate asexplained below.

Also included are algorithms for adjusting dosage during changes inmedication that factor in genetic polymorphisms, and hyperlinks foraccess to on-line information such as PDR and PubMed citations. Theprogram will suggest specific therapeutic alternatives in a drug classwhen requested. The recommended alternatives are chosen by the programso as to avoid the potential DDI detected by the predictive algorithm.

The patient also can have the option of entering insurance informationat Paypoint 1. A CPT code corresponding to the requested access level,is paired with insurer identifiers entered by the patient andcontractual terms stored in the Administrative database 23. CPT codesare the most common currently used service descriptors generallyaccepted by insurers. These mutually-understood reimbursement code dataare represented here by dotted arrow 52 between the Host System operatorand one or more insurer servers 26. Thus, Paypoint 1 can be configuredto permit direct invoicing from the Host System operator to an insurer,again a form of ‘pass through’ invoicing that allows the health careprovider to order Type II pharmacogenetic interpretive services payableby the insurer. Under this model, the Host System operator providesinteractive access and interpretive services to an authorized healthcare provider at no charge, or limited co-pay, to the patient or enduser.

The host server 20 in this embodiment contains an insurance submodule,insurance information stored in the administrative database 23, andalgorithms to detect reimbursable events at Paypoint 1 in GUI 50 and toprocess insurance claims. Certain insurance claims must be authorized inadvance. All necessary information that an insurance company typicallyneeds to process a claim will be included in a request for authorizationto permit a service. The decision by the insurance company willdetermine how the Metabolomics Engine will process a transaction atPaypoint 1. The decision-making process is optionally represented bybidirectional arrow 52. If the insurance company authorizes the service,the system will proceed to offer the authorized service, for exampleaccess to a Type II report function or to a genotype interpretivefunction. If the insurance company denies authorization, however, theuser will be held at Paypoint 1 pending selection of another option, forexample an option to email a customer representative.

Once insurance authorization has been completed, the system processesthe user's query. The insurance submodule, in conjunction withalgorithms associated with Paypoint 1, will detect reimbursable servicesand assign the appropriate reimbursement codes, in conjunction with abilling server such as server 18. A preferred reimbursement code is aCPT code. The CPT code assigned will correspond to a generally approvedfee schedule for professional interpretation of a genetic test result.

In another embodiment, users gaining first access to the MetabolomicsEngine in the course of purchasing genetic testing services from acontract laboratory are converted to direct Host System customers. Inthis new model, “sponsored user” 9, viewing the lab report through userinterface 40, is provided with a sponsored-use hyperlink or URL foraccess to user interface 50. The sponsored-use hyperlink (*), indicatedby arrow 43, when accessed with a password and access code, opens updatapipe 41, which includes a selectable level of interactive access tothe Metabolomics Engine at 44 under control of Paypoint 2. In this way,the contract laboratory customer is now directly accessing theMetabolomics Engine at GUI 50, which offers the user an opportunity toenter personal medical information and view multiple subpages withactive links to in-depth information related specifically andcontextually to the patient history. With this incentive, the customercan chose to continue as a sponsored user, for example for a trialperiod, or can convert to a subscription use (ie, as a “conversionsubscriber” 8 or to a fee-for-access user 7), directly paying the HostSystem operator for the interactive access (Paypoint 2). The system canalso offer links for ordering other genetic testing services from thereferring lab, for example.

Importantly, the reports available at this level of service through GUI50 include: interpretive Type II reports of contextual interpretationrelated to personalized information entered by the patient or endconsumer and stored on the database, such as information about currentprescription regimen, history of smoking, alcohol, and use of herbals.In contrast, the earlier-described Type I lab reports accessed throughuser interface 30 and 40 do not permit entry of patient-specific medicalinformation related to treatment, drugs taken, or patientcharacteristics. The services offered can be endowed with multilevelpermissions with corresponding costs (by configuring Paypoint 2), up toand including the full service benefits discussed in regard to Paypoint1 above. Customers who convert in this manner become increasinglysophisticated in the use of pharmacogenetics in managing their medicalcare. By accessing GUI 50 while consulting with a physician, forexample, the possible patient-specific risks of a new drug can beevaluated in the context of the patient's existing drug regime andgenetic makeup before the prescription is written. Interactive access at44 is thus seen as a natural step in conversion of the customer to fullaccess at datapipe 51, whereby the customer becomes a direct customer ofthe Host System operator and accesses the system through Paypoint 1.

In another embodiment, when drugs are being prescribed that are subjectto substantial genetic variability in metabolism, the algorithms of theMetabolomics Engine will advise the user of potential risks and suggestspecific genetic tests.

The sponsored-user and conversion subscriber portal is secured bymethods known in the art, using passwords, access codes and digitalcertificates, for example. The medical databases are encrypted.

FIG. 2 is a flow diagram illustrating the operation of Paypoint 1 ofFIG. 1. As implemented on a computer system, for example the Host Systemof FIG. 1, an end user such as a physician, other health careprofessional or patient, accesses Host System 20 through GUI 50. Theuser enters a patient identifier and a password or other accessinformation to gain access to a medical record stored on database 23.

The user then enters a list of a plurality of factors to be associatedwith the patient identifier on an interactive webpage, where the factorsare selected from the group consisting of prescription drug usage(s),bioactive substance usage(s), and clinical factor(s). Metabolicphenotype information is also a factor, but is generally entered by aclinical laboratory under the supervision of a pathologist and notgenerally accessible to editing by the patient or end user. The genetictest data (genotype) entered by the laboratory is translated into aphenotype by the Medical Metabolomics Engine (21) and stored in theClinical Records database. This establishes the patient's current drugand substance regimen and any significant clinical factors. On commandof the user, the view is then updated with a prediction assessing thebiocompatibility of the patient-specific data entered (ie. a Type IIreport). Also displayed are warning(s) of any predictedbioincompatibility between the listed factors. The algorithm considersnot only drug-gene interactions but also drug-drug interactions anddrug-clinical factor interactions.

Typically, a predictive algorithm of the type disclosed herein, the TypeII PK predictive algorithm explained in FIG. 5, is used to make theprediction. The service is flagged as a pharmacogenetic interpretiveservice for automated billing. At Paypoint 1, the system flags theoperation in step 4 and automatically generates an invoice to a thirdparty payor (step 4) or to a customer.

At Paypoint 1, access is a billable service, and the user has multiplechoices in selecting a reimbursement method. In one option, the user canenter a financial instrument at a paypoint associated with said secondgraphical user interface. Of particular interest is a business model inwhich payment is received from a third party payor for the end user'saccess to the system. For example an insurance payor 26 may be billed bythe Host System directly. One aspect of this is indicated in FIG. 1 byarrow 52, whereby the backoffice administration of the Host Systemoperator has prearranged contractual understandings and a table ofaccepted billing codes that are used to monetize the value of thespecific characteristics of the Type II report functions utilized by theuser.

FIG. 3 is a flow diagram illustrating the operation of Paypoint 2 ofFIG. 1. Paypoint 2 is more complex but also involves GUI 50. A majoradvantage of this automated business method is the ability to convert acontract client's customer to a direct customer of the Host System,while providing win-win value to the client laboratory. As implementedon a computer system, a client laboratory in the business of providinggenetic testing services accesses the Host System 20 at first GUI (GUI30). The client typically has a contract or subscription agreement toaccess the Host System. In step 1, the client (typically a laboratorytechnician) enters a patient identifier and a genetic test result(typically a genotype or “star data”) associated with the patient andstores that information in Clinical Records database 23. The systemmerges records associated with a single patient identifier. The clientthen enters a command to the host server that generates a test report(Type I) using host system resources (step 2); the test result includesa phenotypic interpretation of the genotype(s), a list of drugs forwhich the drug's metabolism is likely to be adversely impacted by thephenotype (ie. potential drug-gene interactions), a hyperlink to asecond GUI (GUI 50), and a password or access codes whereby the customer(such as a patient, physician) can access the Host System (20) directly.Typically, in step 3, the client transmits the report to the customer 9,optionally via a Host System webserver. Encryption is commonly used tosecure the data during transmission and storage of passwords.

Operation of the PK predictive algorithm used in preparation of Type Ireports is explained in more detail in FIG. 4.

In step 4, when the customer 9 accesses the Host System directly at GUI50, and enters the patient identifier and password, the Host Systemprovides an interactive webpage (the opening screen for a Type IIreport). This screen is illustrated in FIG. 10. The user has the optionof entering a plurality of clinical factors associated with the patientidentifier, for example prescription drug(s) and bioactive substance(s)taken, and other clinical factor(s) (ie. “patient characteristics”) suchas age, pregnancy, smoking, and so forth. Customers 9 may haverestrictions on levels of access. In one embodiment the customer is notauthorized to delete or add phenotypes. This prevents the user fromgiving unauthorized access to guest users. After entering the list ofdrugs and other factors, the patient can give his or her healthcareproviders access to the complete patient record from any computer or PDAwith internet access. The medical record is stored on the Host Systemserver and requires a password or other access code for authorizedaccess.

A Type II PK predictive algorithm (discussed below, FIGS. 5-7) is thenrun. For any metabolic pathway associated with a drug or substance onthe list entered by the user, the system identifies any drug-drug,drug-bioactive, drug-clinical factor (or “drug-patient characteristic”),drug-phenotype and substance-phenotype potential “interaction pair(s)”,and identifies the impacted substrate (the “victim”). One drug (the“culprit”), for example, will inhibit the metabolism of another,resulting in greater exposure of the patient to the victim drug. Anoverdose can occur if the drugs are co-administered. The algorithm thencalculates a change in the AUC of the impacted substrate and annotates atable with this information (see FIG. 6, subroutine A, and FIG. 8). Thetable may also contain hierarchically selected warnings drawn from theliterature or based on the PK algorithm result (FIG. 7, subroutine B).This information is assembled into a webpage (a Type II report) at GUI50 and presented to the customer 9. The exchange is flagged at Paypoint2 and the information about the transaction is forwarded to a billingserver.

Paypoint 2 also offers the user the ability to configure thetransaction. In one model for reimbursement, the customer is offeredseveral methods of paying for the data ranging from subscription and“pay-per-ping” with a credit card to wholesale bulk access at adedicated GUI. A preferred reimbursement model permits the customer(often either the clinic or a physician) to ‘pass through’ the cost ofthe interpretive access and consultation to a third party insurer. Inthis case, an interactive screen associated with Paypoint 2 allows theuser to enter insurance information. Customers of type 9 may convert tocustomers of type 8, 7 or 6 by this method.

Note that in this model the customer is essentially handed off from theclient laboratory to the Host System operator. Given the synergies ofthe model, the client laboratory also benefits by this arrangement.

FIG. 4 is a flow diagram illustrating the production of a Type I reportand the operation of Paypoints 3, 4 and 5 of FIG. 1. In this embodiment,a “pass-through” cost model for the client laboratory is developed.

The client laboratory accesses the Host System 20 and enters a patientidentifier, laboratory identifier, and a genetic test result in adatabase. A command from the client causes the Lab Report Engine 24,operating with the Metabolomics Engine 21, to produce a Type Ilaboratory report formatted with the client's logo. The test reportincludes a phenotypic interpretation of the genetic test result and alist of drugs likely to be associated with a Change % AUC based on thephenotype. This is a drug-gene interaction report. The report is thensecurely transmitted to the consumer, either directly by the Host Systemat GUI 40 or by the laboratory server. The laboratory flags thetransmission (step 3) as a billable service either at Paypoint 5,submitting an invoice for reimbursement to a third party payor for thephenotypic interpretation service, or at Paypoint 3, submitting aninvoice to the customer. In the preferred method, (step 4) thesubscribing laboratory recovers the costs of accessing host systemresources by generating a “pass-through” billing based on areimbursement code, such as a CPT code, corresponding to a generallyapproved fee for a diagnostic pathology fee, which according to apreferred embodiment of the method, is paid by the patient's insurance.The client laboratory pays the Host System operator for access to thehost system.

The steps of the PK predictive algorithm in FIG. 4 illustrate the use ofphenotype data to calculate CP for each drug. In step B, drugs areselected for the sublist from the database (22) by therapeutic class,Medicare Part D reimbursement eligibility, regulatory approval specificto the jurisdiction, frequency of prescription usage data, and so forth.The list is unbundled by adding components of drug mixtures, prodrugs,enantiomers and metabolites. Drug-gene interaction pairs are thenidentified. Literature concerning the phenotype is searched to determinethe intensity INTX of inhibition or induction associated with theimpacted metabolic route. Parallel alternate metabolic pathways are alsoevaluated for FRACTION R_(1/1-n) (metabolic throughput on the affectedroute R divided by total metabolic throughput by all parallel pathwaysR_(1-n)). The equation for CP “change points” can then be solved:

CP=INTX*(R _(1/1-n)),

This calculation is performed only if the patient phenotype is abnormal.For each potential victim drug, the net affect of all phenotypes iscalculated by summing CP:

ΣCP=(CP _(R1) +CP _(R2) + . . . CP _(Rn))

The calculations are stored in a summary table of results. The value ΣCPis converted to a Change % AUC using a look up table such as shown inFIG. 8. If the change in CP is less than a threshold level and there areno clinical warnings in the notes in the database, then the drug isdeleted from the sublist. This process is repeated for all drugs and thedrugs remaining on the sublist are tabulated for presentation in theType I lab report as shown for example in FIG. 9. The Type I lab reporttypically consists of a patient identifier, laboratory identifier, aphenotypic interpretation of the genetic test result(s), and thepredicted drug-gene interactions from the sublist, also showingpredicted Change % AUC (up or down). This report can be formatted sothat it appears consistent with the look of other documents or webpagesof the client laboratory.

FIG. 5 is a flow diagram outlining the major operations of a PKpredictive algorithm used in the preparation of Type II reports of theexamples. In step 6, any potential interaction pairs that can beassociated with a drug interaction are identified. These includedrug-drug, drug-bioactive, drug-clinical factor, drug-phenotype, andsubstance:phenotype interactions.

In step 1, a factors list is entered and tabulated. This list consistsof drugs, bioactives, factors taken from the clinical history, andgenotypes or phenotypes. In step 2, the list is then factored or“unbundled” by converting any drug mixtures to their individual drugcomponents, identifying prodrugs, replacing racemic substances whichhave relevant enantiomers with the enantiomers (for example warfarin hasr and s isomers with markedly differing metabolism and bioactivity), andadding to the table any pharmacologically active metabolites. A classmembership may also be identified. Typically the genotype is already inthe clinical records database and the translation to phenotype hasalready been made, but it may be done so in step 3 if not alreadycompleted.

In step 4, inhibitors and inducers on the list are identified.Inhibitors and inducers may act on more than one metabolic route. Anintensity index expressing the degree of induction or inhibition of eachmetabolic route is also tabulated. Victim substances are then identifiedin step 5 and associated with the metabolic routes. These operations areperformed by accessing a list of the metabolic pathways for eachsubstance, and then ascertaining all inducers and inhibitors of thosepathways contained in the factors list. The resulting table contains all“interaction pairs” relevant for each metabolic pathway. Eachinteraction pair includes one victim and one culprit substance or factor(step 6).

The computer then makes, in step 7, quantitative interactioncalculations for each interaction pair:

CP=INTX*(R _(1/1-n)),

where CP″ is the “change point” score for each metabolic route R of eachdrug identified as the victim in the interaction pairs table, INTX is anintensity of interaction index derived from clinical and laboratorystudies of individual substances and genes (or factors), R_(1/1-n)denotes R₁/(R₁+R₂ . . . R_(n)), where R_(n) refers to one of the set ofparallel metabolic routes taken by the substance and R_(1/1-n) is theproportion of metabolism that flows through pathway R₁, and so on. Thisquantifies the relative change in AUC of the victim substance resultingfrom one interaction on one metabolic pathway. The change point score CPcan be positive or negative, representing the opposing effects ofinduction and inhibition.

In the next calculation, step 8, the CPs for all metabolic routes R foreach interaction pair are summed:

ΣCP=(CP _(R1) +CP _(R2) + . . . CP _(Rn))

and the calculations are stored in a summary table of results.

For each drug or substance, the change in AUC can be complex, resultingfrom multiple interactions. In a preferred embodiment, for eachinteraction pair, a ΣCP score is calculated that quantitates oneparticular interaction, and the ΣCP scores of all the interactions arethen summed across all pairs for a common victim to determine the netchange in drug blood level and clearance for the victim of multipleinteractions.

Step 9 converts raw change values to Change % AUC values for each victimsubstance (See FIG. 8. FIG. 8 is representative of the look-up processwhereby ΣCP is converted to a percent change in blood level). When theresults are compared with published clinical studies from theliterature, this composite method is surprisingly effective at makingaccurate predictions. We have found that these results correlate wellwith literature studies where available. The PK predictive algorithm'saccuracy can be tested by comparing the AUC changes it predicts forpairs of interacting drugs with literature reports of clinical studiesof the same pairs.

In step 10, the algorithm may comprise a subroutine for collatingliterature-derived reports related to each interaction pair identified.Relevant clinical notes in the database bibliographical records arecalled up and attached to the main results table. Literature clinicalstudy notes germane to the substance class or “class membership” arealso identified if desired. The algorithm creates a list of the clinicalstudies and their associated scientific confidence ratings. In instanceswhere research confirms the absence of interaction, an appropriate notepresents this information.

The result is a prediction of the effect of the interactions on thevictim substance AUC, its blood level and clearance time, as reflectedin the change in its pharmacokinetics as a result of the secondimpacting “culprit” substance or factor. The prediction is made even ifsupporting clinical studies are not available.

In subroutine A (FIG. 6) the impact of each metabolic route R_(n) on thevictim substrate is quantified as the product of the interactionintensity INTX with the fractional metabolism of the victim by themetabolic route over the total metabolism by all parallel metabolicroutes R_(1/1-n).

FIG. 7 is a detail showing the steps of subroutine B. Subroutine Bcompares the prediction of an interaction with literature citationsstored in the databases. The warnings are ranked by severity and themost significant warning is displayed.

The warnings are displayed according to the following rules. Aftertabulating all warnings according to priority from highest to lowest,the highest priority warning on the list is displayed on the Type IIinteraction report.

A. Major interaction warnings based on a clinical study

B. Reported interaction based on a clinical study

C. Major interaction warning predicted by the PK predictive algorithm

D. Reported lesser interaction based on the PK predictive algorithm

FIG. 8 shows a table used in the PK predictive algorithm to convert ΣCPto a percent change in AUC. Showing are ranges of point scorescorresponding to ΣCP calculations (81, column 1), an interpretation ofthe predicted qualitative effect (82, column 2), a change index used tobuild column 2, and a predicted % change in AUC (84, column 4). Notethat the Change % AUC can be up or down (plus or minus sign).

Returning to FIG. 5, in Step 11 the Host System builds a webpage thatreports the Type II predictive analysis back to the user. The user mayin turn modify the input by selecting an alternate drug not linked to aninteraction (and potential ADR) and run the analysis again. All clinicaldata is stored in the system.

A refinement in the PK predictive algorithm of FIG. 5 is as follows.Class memberships of the victim and culprit drugs are identified. Thedatabase is then searched for other members of the same classmembership. The commonality of these class memberships is a shared sideeffect. If found, the two drugs or substances of the class are annotatedin the report. This is done because side effects can be additive if thetwo drugs are co-administered. Thus the algorithm can also detect DDIseven when a genetic interaction is not relevant.

Interestingly, the patient can share this service with the physician, orvice versa. During an office visit, patient and physician can model anddiscuss alternative therapies and use the system to explain anyunexpected adverse reactions when the patient tries a medication,ordering genetic testing if necessary. The system will update the TypeII report each time it is accessed.

Prior art reports do not contain a list of drugs sorted by drug classthat have been shown by predictive algorithm or review of the clinicalliterature to interact with the listed phenotype. Instead there is ablack-box warning, “Do not alter the dosage amount or schedule of anydrug you are taking without first consulting your doctor orpharmacists.” This warning is necessary given the risk of DDI and ADR inthis patient phenotype, but is of little value at the point of care inprescribing safely. There is thus a need for improvement over the priorart, a need met by the algorithms of the current invention.

Avoidance of a major ADR is a medical cost savings, but requires the‘pass through’ reimbursement functions of the system to be generallyaccessible to physicians. In this system, the user is again interfacingdirectly with the Metabolomics Engine at GUI 50 and may be preferred“full-subscription” customers 6, “conversion” subscribers 8, “trial use”or “sponsored use” users 9, or “fee-for-service” customers 7, forexample. Paypoint 2 is provided with a means for flexibly selecting asuitable payment option, including insurance options for pass-through ofcosts, thus ensuring that the drug interaction warning and supplementalinformation is made available where and when needed.

In a preferred embodiment, this advanced level of interactiveinformation is made accessible to the end user through a hyperlink oraccess code appended to a Type I Lab Report such as is generated by theLab Report Engine at GUI 30 or 40. The hyperlink is a link tointeractive GUI 50 with more detail on 1A2 Hyperinduction related to anypair of drugs (or drug:herbal pairs, etc.), and a user can proactivelyenter their own prescription information to determine whether there is acontraindication before prescribing or taking them.

FIG. 9 is a view of a sample Type I lab report 170 with sponsored-usehyperlink 173. The report includes an interpretation of a phenotype 171associated with the below—named genotype. In this example a 2D6 poormetabolizer is associated with a CYP2D6 *3/*4 genotype, as determined bygenetic testing. At the bottom of the report, an extended list 172 (heretruncated) of drugs of predicted interactions with the named phenotypeis given, allowing the end user to identify any potential concerns forfollow up.

Also provided is a hyperlink 173 for more personalized information. Inthis embodiment, the hyperlink corresponds “sponsored user” hyperlink ofthe type shown in FIG. 1, element 43 (*) and is linked to GUI 50 atPaypoint 2. At GUI 50, various structured types of payment for accessare available, but by providing a trial or sponsored service at Paypoint2, genetic testing customers 9 are converted to use of the Host System.In a preferred business model, a “sponsored user” hyperlink 43 takes theconsumer, whether a physician, patient, or lay caregiver, to an enhancedgraphic user interface (GUI 50) with improved paypoint capability(Paypoint 2). With hyperlinks of this sort, a trial period is offered sothat customers discovering the enhanced services of GUI 50 through thesponsored user hyperlink access will be encouraged to try out theservice and convert to a direct financial relationship with the HostSystem operator, opting for the full or layered service of Paypoint 1.Payment options can comprise a fee-for-service, subscription, trial,discount, wholesale, or other relationship whereby the user accesses thetools of GUI 50. In a preferred embodiment, Paypoint 1 offers a ‘passthrough’ feature convenient for medical health care professionals whoneed access to pharmacogenetic testing data, such as when writingprescriptions, but who would not choose to pay for those services whenundertaken on a patient's behalf. The patient can, for example, accessthe service while meeting with a health care provider, to ensure thatany prescriptions written is likely to be compatible with other patientfactors already entered in the system, and optionally, bill the on-lineconsultation services to an insurer.

The predicted drug-drug interactions have been reassorted by therapeuticclass and when accessed, the list is much more comprehensive, spanning 5pages (not shown). The report states, “These genetype-based drugmetabolism tables are generated from the GeneMedRx drug interactioncomputer program, which is based on a compilation of information foundin the medical literature and interpreted by the use of a computeralgorithm. The tables are to be used as a tool to provide decisionsupport, consultation and advisory input to clinical care by medicalprofessionals.” Contrast this with the black box warning above.

FIG. 10 is a representative view of a interactive screen 180 titled“Drug-Drug and Gene-Drug Interactions” and begins with a note to “starthere” (181). This is provided as an example of a portal to the Type IIGUI. The end user (a patient or patient's representative) begins byentering a patient regimen at window 182. The selections can be enteredby selecting factors from the list shown in window 187. The regimenconsists of prescription drugs being taken and other patient factors asmay be configured by the system operator. Notes regarding any relevantclinical history can also be entered in window 184. Information aboutthe patient's phenotype is displayed at 184. The phenotype is generallyentered by a contract laboratory providing genetic testing services andis stored on the Host System without need for re-entry by the patient.Sublists (e.g. “herbals”, bullet 185) are provided for entry of otherfactors, such as herbals, over-the-counter drugs, and foodstuffs knownto interact in drug metabolism. There is also an option 186 to makereferrals. Once this information is entered, we turn to FIG. 11 for thenext step—step three.

FIG. 11 shows a “check interactions” button 190 displayed on the GUI 50interactive website. Also shown is a convenience function 191 forordering genetic testing services. In one embodiment, this orderingfunction is a referral of the user back to the laboratory that providedthe genetic test result which in turn brought the user to GUI 50. It mayalso include links to other genetic testing services, such as paternityservices.

FIG. 12 is an “interaction report” (ie. a Type II report) generated byGUI 50. The report 200 is sent to the user in response to a command tocheck interactions as shown in FIG. 11. This Type II functionality ischaracteristic of GUI 50. The report describes a major interaction 201(known in the clinical literature) between tamoxifen and paroxetine,also predicted by the PK predictive algorithm, for a 2D6 intermediatemetabolizer phenotype 202. The interaction between tamoxifen andparoxetine is both a drug-drug interaction, paroxetine the victim andtamoxifen the culprit, and also a drug-gene interaction; both drugs areimpacted by the 2D6 intermediate metabolizer phenotype. Addedinformation about the mechanism and notes to hyperlinks for in-depthinformation and self-directed search are also provided. Note that theuser can click on a hyperlink 203 to return to and enter or edit thelist of drugs substances (and other factors) that are part of thepatient's current treatment regimen, perhaps selecting an alternatedrug.

FIG. 13 is a webpage 210 used at Paypoint 1 or Paypoint 2 to configurereimbursement options. The user is asked for more information whichcorresponds to a market segment. By selecting the appropriate bulletfrom the list 211 (bracket), the user is directed to follow-on pageswith the appropriate functionality. Data is entered that allows thesystem to process financial transactions covering reimbursement for thesystem's data exchanges with the user. A patient who selects bullet 212,for example, is offered additional choices of credit card or entry ofinsurance information, and the credit card or insurance is then verifiedfor authorization to conduct the transaction. Various trialsubscriptions 213 are also optional, both for medical professionals andfor patients. After completion of the financial information, the user isthen directed to a start page for selection of permissible tasks. Notall users have equal access to core and extended functionality. Userswho have a sponsored subscription will be directed to a webpage to enterthe appropriate passwords or access codes before being granted access tosystem functions.

In one aspect, the invention is directed at a Type II predictivealgorithm and apparatus or method for performing the operations of thepredictive algorithm. The invention comprises a method or apparatus forpredicting a substance-factor interaction, including drug-drug anddrug-gene interactions, and comprises steps for

-   -   a) providing a graphical user interface, a host system, and a        database, wherein said graphical user interface is configured        for:        -   i) accessing a patient record in said database, said patient            record comprising a patient identifier and a first patient            phenotype;        -   ii) entering one or more factors into a list associated with            said patient identifier, wherein said one or more factors            are selected from the group consisting of prescription drug,            substance, and personal characteristic;    -   b) providing a predictive algorithm implemented on said host        system, said algorithm having instructions for performing        operations on said database, said patient record and said        associated list, wherein said operations comprise:        -   i) unbundling the list, thereby forming an unbundled list;        -   ii) determining each factor on the unbundled list that is an            inhibitor or an inducer; and assigning an intensity index            INTX to each said inhibitor and inducer;        -   iii) selecting from the unbundled list a sublist of victims,            where a victim is a factor having the property of being a            metabolic substrate of one or more metabolic routes Rn;        -   iv) identifying each metabolic route associated with said            sublist of victims;        -   v) identifying each interaction pair associated with said            each metabolic route, each interaction pair consisting of a            victim and a culprit;        -   vi) for each victim of said each interaction pair;            calculating a CP score by multiplying an intensity index            INDX associated with the culprit times a metabolic            throughput proportion R1/1−n, where R1/1−n is calculated as            the metabolic throughput of said each metabolic route Rn            divided by a sum of the throughput of all metabolic pathways            acting on the victim in parallel;        -   vii) summing the CP scores for each victim and for each            interacting pair, and tabulating the sums ΣCP;        -   viii) computing a change percent AUC for each victim and for            each interacting pair;        -   ix) displaying a Type II report tabulating patient            identifier, patient phenotype, factors entered in said list,            and change % AUC for each victim; and,        -   x) flagging the Type II report as a service.

The invention is adapted for predicting interactions between a pluralityof substance-factors where there are a plurality of victims. In one TypeII method, the sublist of victims comprises a plurality of victims. Inanother Type II method, the patient record comprises a plurality ofpatient phenotypes. In another Type II method, the substance-factorinteraction comprises a drug-gene interaction, a drug-drug interaction,or a combination thereof.

Type II methods also can include provision for identifying anddisplaying literature notes and warnings. The algorithms furthercomprise a subroutine, said subroutine having instructions forperforming operations on said database, said patient record and saidassociated list, wherein said operations comprise:

-   -   i) accessing the database and identifying notes or warnings        compiled from published reports of an interaction between said        first victim and said culprit;    -   ii) if no notes or warnings compiled, reporting said change        percent AUC identified with said first victim and said culprit;        and,    -   iii) if notes or warnings compiled, reporting said notes or        warnings identified with said first victim and said culprit.

Type II methods can also include provision for assessing classmembership, wherein the operations further comprise:

-   -   i) for any prescription drug or substance on said list,        accessing the database and identifying a class membership,        wherein said class membership is defined by a side effect        produced by all members of the class;    -   ii) for any class membership identified herein, accessing the        database and identifying any factors from the list having said        class membership in common; and,    -   iii) reporting said factors in common, with a note advising that        said side effect can be additive.

Type II methods include tools for making alternate drug selections. Theoperations further comprise:

-   -   i) for any potential interaction pair for which said change        percent AUC exceeds a threshold value, accessing said database        and identifying a therapeutic class associated with said first        victim;    -   ii) identifying an alternate member of said therapeutic class        and calculating an alternate percent change AUC for the        alternate member;    -   iii) reporting the alternate member in a listing of interactive        selection of alternates if the alternate percent change AUC does        not exceed a threshold value.

The methods and apparatus also include provision for generating a Type Ilab report, which will predict a drug-gene interaction when given aphenotype and a list of drugs. The method or apparatus comprises agraphical user interface, an host system, and a database, wherein saidgraphical user interface is configured for:

-   -   i) entering a patient record in said database, said record        comprising a patient identifier and a first patient phenotype;    -   ii) providing a predictive algorithm implemented on said host        system, said algorithm having instructions for performing        operations on said database, said patient record and said        associated list, wherein said operations comprise:    -   iii) accessing a list of drugs on a database, said drugs        comprising prescription drugs and substances, and unbundling the        list, thereby compiling an unbundled list;    -   iv) determining an inhibition or an induction of at least one        metabolic route Rn associated with said first phenotype; and        assigning an intensity factor INTX to said inhibition or        induction;    -   v) selecting from the unbundled list a sublist of victims, where        a victim is a member of said unbundled list having the property        of being a metabolic substrate of said metabolic route Rn        associated with said first phenotype;    -   vi) for each victim in said sublist, calculating a CP score by        multiplying an intensity index INDX associated said inhibition        or induction of said at least one metabolic route Rn associated        with said first phenotype times a metabolic throughput        proportion R1/1−n, where R1/1−n is calculated as the metabolic        throughput of said metabolic route Rn divided by a sum of the        throughput of all metabolic pathways acting on the victim in        parallel;    -   vii) from the CP score of the preceding step, computing a change        percent AUC;    -   viii) discarding any drugs in the sublist if the change % AUC is        below a threshold value; thereby forming a summary table;    -   ix) displaying a Type I report tabulating patient identifier,        patient phenotype, laboratory identifier, and change % AUC for        each victim in said summary table; and,    -   x) flagging the Type I report as a service.

Type I methods are also adapted to predicting a drug-gene interactionfor a plurality of patient phenotypes, and said operations comprisecalculating a ΣCP score for each victim, where said ΣCP score is the sumof the CP scores over said plurality of phenotypes, and computing change% AUC for each victim from the ΣCP score in said summary table.

The methods are also adapted as business methods. In one embodiment theinvention is a business method for obtaining reimbursement forpharmacogenetic interpretive services, which comprises:

-   -   a) implementing a billing server configured for detecting a flag        associated with a service of claim 1;    -   b) invoicing a payor associated with the patient identifier, and        optionally, said payor is a third party payor and said billing        server comprises an insurance submodule.

In another embodiment the invention is a business method for obtainingreimbursement for pharmacogenetic interpretive services, whichcomprises:

-   -   a) implementing a billing server configured for detecting a flag        associated with a service of claim 7;    -   b) invoicing a payor associated with the patient identifier, and        optionally, said payor is a third party payor and said billing        server comprises an insurance submodule.

The methods are adapted for operation at Paypoint 1. Conceived is abusiness method, as implemented on a computerized host system, forobtaining automated third-party reimbursement by providingpharmacogenetic interpretive services for preventing a possible adversedrug reaction, comprising the steps of:

-   -   a) providing a first user with a means for accessing a host        system and a means for entering a patient record comprising a        patient identifier of a patient and a genotype associated with        said patient identifier, and thereupon    -   b) on command of said first user, translating said genotype into        a phenotype and entering said phenotype in said patient record;    -   c) providing a second user with a means for entering a plurality        of factors into the patient record, wherein the factors are from        the group consisting of prescription drug(s) prescribed,        substance(s) used, clinical factor(s);    -   d) upon command of said second user, computing a change % AUC        for any interacting pairs of factors entered, computing a        prediction warning of a potential bioincompatibility between        said interacting pairs, wherein said prediction is made by a PK        predictive algorithm, and displaying a report; and,    -   e) flagging the prediction as a billable service; and        wherein the method is further characterized in that        reimbursement is made according to a prearranged fee schedule        between an operator of the host system and a third party payor        contracted by said patient to pay for said billable service.

The methods are also adopted for operation at Paypoint 3, 4 and 5.Conceived is a business method, as implemented on a computerized hostsystem, for obtaining automated third-party reimbursement by providingpharmacogenetic interpretive services for preventing a possible adversedrug reaction, comprising the steps of:

-   -   a) providing a first user with a means for accessing a host        system and a means for entering a patient record comprising a        patient identifier of a patient and a genotype associated with        said patient identifier; and thereupon    -   b) on command of said first user, translating said genotype into        a phenotype and entering said phenotype in said patient record;    -   c) upon command of said first user, selecting a list of drugs,        computing a change % AUC for any interacting pairs of factors        entered, computing a prediction warning of a potential        interaction between said drug and phenotype, wherein said        prediction is made by a PK predictive algorithm; and preparing a        Type I lab report comprising a pharmacogenetic interpretive        service;    -   d) upon command of said first user; transmitting said report to        a customer, wherein said customer is a customer of said first        user and flagging said transaction to a billing server operated        by said first user;    -   e) receiving a reimbursement from said first user for access to        said host system;        wherein the method is further characterized in that the billing        server operated by said first user automatically bills for said        pharmacogenetic interpretive service. Optionally, the billing        server is further characterized in that said billing server        automatically bills a third party payor contracted by said        patient to pay for said pharmacogenetic interpretive service.

Customer conversion methods are also conceived. In one embodiment, weconceive a business apparatus for obtaining automated reimbursement forpharmacogenetic interpretive services, said apparatus comprising acomputerized host system operated by a host system operator and having ameans for data storage, a means for data processing, a means fornetworking, a first graphical user interface for access to the hostsystem by a first user, a second graphical interface for access to thehost system by a second user, wherein said apparatus is configured withmeans for:

-   -   a) under control of said first user, entering and storing a        laboratory identifier, patient identifier and a genetic test        result comprising a patient genotype in a patient record on said        first graphical user interface of said host system, said first        user being a laboratory with a client relationship with said        host system operator;    -   b) on command of said first user, performing a phenotypic        interpretation of said genotype entering said phenotype in said        patient record on said host system;    -   c) on command of said first user, using a first predictive        algorithm resident in said host system to prepare a predictive        drug-gene interaction report (a Type 1 report);    -   d) under control of said host system operator, appending a        hyperlink to said predictive drug-gene interaction report, said        hyperlink having the property of linking to said second        graphical user interface;    -   e) on command of said first user, transmitting said predictive        drug-gene interaction report with appended hyperlink to said        second user, said second user being a patient or a responsible        medical care provider;    -   f) under control of said second user, opening said second        graphical user interface when said second user accesses said        appended hyperlink;    -   g) under control of said second user, editing said patient        record to add a list of factors to be associated with said        patient identifier, wherein said factors are selected from the        group consisting of prescription drug, substance, and clinical        factor;    -   h) on command of said second user, using a second predictive        algorithm resident in said host system to prepare a predictive        drug-drug and drug-gene interactive report (a Type II report),        flagging said predictive drug-drug and drug-gene interactive        report as a pharmacogenetic interpretive service, and displaying        said Type II report to said second user on said second graphical        interface;    -   i) in response to said flag, billing said second user for said        pharmacogenetic interpretive service, wherein said second user        has preselected a payment method by entering a financial        instrument at a paypoint associated with said second graphical        user interface.

Optionally, said paypoint (typically paypoint 2) is configured forentering a financial instrument to be used as payment for saidpharmacogenetic interpretive service. The financial instrument may beselected from insurance information, credit card information, on-linedebit information, sponsored use access code, trial use access code, orsubscription information.

In another embodiment of the conversion methods, conceived is a businessmethod, as implemented on a computer host system, for obtainingautomated third-party reimbursement by providing professionalinterpretation of a genetic testing result, comprising the steps of:

-   -   a) As a service to a client, said client having a customer, said        client having provided a genetic test service for a patient on        request of said customer,        -   1) providing said client with a means for accessing a host            system (20) at a first graphical user interface (30) and a            means for entering a customer identifier and a genetic test            result associated with the customer, and storing that            information in a database on the host system;        -   2) on command of the client, generating a test report for            the client; the test result including i) a phenotypic            interpretation of the genetic test result associated with            that customer, ii) a list of drugs likely to be associated            with an adverse drug reaction when administered to said            patient, iii) a “sponsored user” hyperlink to a second            graphical user interface, and iv) a password;        -   3) on command of the client, securely transmitting the test            report to the customer, said transmission constituting a            service billable by the client to a third party payor;    -   b) when the customer accesses the “sponsored user” hyperlink and        enters the password at a paypoint (2),        -   1) providing the customer with direct access to an            interactive webpage on the second graphical user interface            (50) and allowing the customer to enter a plurality of            clinical factors to be associated with the patient            identifier and patient phenotype, including clinical factors            selected from the group consisting of prescription drug(s)            prescribed, bioactive substance(s) used, and clinical            history factor(s),        -   2) securely updating the interactive webpage with a            prediction evaluating the biocompatibility between the            clinical factors associated with the patient identifier and            with the patient phenotype;        -   3) displaying a warning of any predicted bioincompatibility;            and,        -   4) flagging the access as a billable service, and receiving            reimbursement for the direct access to the system, and            further receiving reimbursement from the client for access            to the system.

In this latter conversion model, paypoint 2 is configured with a menufor choosing a method of payment selected from a) free trial period withauthorization code, b) credit card payment for access, c) debit cardinformation; d) entry of insurance information and on-line authorizationfrom the insurer, e) subscription payment for access, f) sponsored usewith authorization code, and g) wholesale group contract payment foraccess.

Unless the context requires otherwise, throughout the specification andclaims which follow, the word “comprise” and variations thereof, suchas, “comprises” and “comprising” are to be construed in an open,inclusive sense, that is, as “including, but not limited to”.

Reference throughout this specification to “one embodiment” or “anembodiment” means that a particular feature, structure or characteristicdescribed in connection with the embodiment is included in at least oneembodiment of the present invention. Thus, the appearances of thephrases “in one embodiment” or “in an embodiment” in various placesthroughout this specification are not necessarily all referring to thesame embodiment. Furthermore, the particular features, structures, orcharacteristics may be combined in any suitable manner in one or moreembodiments.

While the above is a description of the preferred embodiments of thepresent invention, it is possible to use various alternatives,modifications and equivalents. Therefore, the scope of the presentinvention should be determined not with reference to the abovedescription but should, instead, be determined with reference to theappended claims, along with their full scope of equivalents.

1: A method for predicting a substance-factor interaction, whichcomprises: a) providing a graphical user interface, a host system, and adatabase, wherein said graphical user interface is configured for: i)accessing a patient record in said database, said patient recordcomprising a patient identifier and a first patient phenotype; ii)entering one or more factors into a list associated with said patientidentifier, wherein said one or more factors are selected from the groupconsisting of prescription drug, substance, and personal characteristic;b) providing a predictive algorithm implemented on said host system,said algorithm having instructions for performing operations on saiddatabase, said patient record and said associated list, wherein saidoperations comprise: i) unbundling the list, thereby forming anunbundled list; ii) determining each factor on the unbundled list thatis an inhibitor or an inducer; and assigning an intensity index INTX toeach said inhibitor and inducer; iii) selecting from the unbundled lista sublist of victims, where a victim is a factor having the property ofbeing a metabolic substrate of one or more metabolic routes Rn; iv)identifying each metabolic route associated with said sublist ofvictims; v) identifying each interaction pair associated with said eachmetabolic route, each interaction pair consisting of a victim and aculprit; vi) for each victim of said each interaction pair; calculatinga CP score by multiplying an intensity index INDX associated with theculprit times a metabolic throughput proportion R_(1/1-n), whereR_(1/1-n) is calculated as the metabolic throughput of said eachmetabolic route Rn divided by a sum of the throughput of all metabolicpathways acting on the victim in parallel; vii) summing the CP scoresfor each victim and for each interacting pair, and tabulating the sumsΣCP; viii) computing a change percent AUC for each victim and for eachinteracting pair; ix) displaying a Type II report tabulating patientidentifier, patient phenotype, factors entered in said list, and change% AUC for each victim; and, x) flagging the report as a service. 2: Themethod of claim 1, wherein said sublist of victims comprises a pluralityof victims. 3: The method of claim 1, wherein said patient recordcomprises a plurality of patient phenotypes. 4: The method of claim 1,wherein said substance-factor interaction comprises a drug-geneinteraction, a drug-drug interaction, or a combination thereof. 5: Themethod of claim 1, wherein said algorithm further comprises asubroutine, said subroutine having instructions for performingoperations on said database, said patient record and said associatedlist, wherein said operations comprise: i) accessing the database andidentifying notes or warnings compiled from published reports of aninteraction between said first victim and said culprit; ii) if no notesor warnings compiled, reporting said change percent AUC identified withsaid first victim and said culprit; and, iii) if notes or warningscompiled, reporting said notes or warnings identified with said firstvictim and said culprit. 6: The method of claim 1, wherein saidoperations further comprise: i) for any prescription drug or substanceon said list, accessing the database and identifying a class membership,wherein said class membership is defined by a side effect produced byall members of the class; ii) for any class membership identifiedherein, accessing the database and identifying any factors from the listhaving said class membership in common; and, iii) reporting said factorsin common, with a note advising that said side effect can be additive.7: The method of claim 1, wherein said operations further comprise: i)for any potential interaction pair for which said change percent AUCexceeds a threshold value, accessing said database and identifying atherapeutic class associated with said first victim; ii) identifying analternate member of said therapeutic class and calculating an alternatepercent change AUC for the alternate member; iii) reporting thealternate member in a listing of interactive selection of alternates ifthe alternate percent change AUC does not exceed a threshold value. 8: Amethod for predicting a drug-gene interaction, which comprises: a)providing a graphical user interface, an host system, and a database,wherein said graphical user interface is configured for: i) entering apatient record in said database, said record comprising a patientidentifier and a first patient phenotype; b) providing a predictivealgorithm implemented on said host system, said algorithm havinginstructions for performing operations on said database, said patientrecord and said associated list, wherein said operations comprise: i)accessing a list of drugs on a database, said drugs comprisingprescription drugs and substances, and unbundling the list, therebycompiling an unbundled list; ii) determining an inhibition or aninduction of at least one metabolic route Rn associated with said firstphenotype; and assigning an intensity factor INTX to said inhibition orinduction; iii) selecting from the unbundled list a sublist of victims,where a victim is a member of said unbundled list having the property ofbeing a metabolic substrate of said metabolic route Rn associated withsaid first phenotype; iv) for each victim in said sublist, calculating aCP score by multiplying an intensity index INDX associated saidinhibition or induction of said at least one metabolic route Rnassociated with said first phenotype times a metabolic throughputproportion R_(1/1-n), where R_(1/1-n) is calculated as the metabolicthroughput of said metabolic route Rn divided by a sum of the throughputof all metabolic pathways acting on the victim in parallel; v) from theCP score of the preceding step, computing a change percent AUC; vi)discarding any drugs in the sublist if the change % AUC is below athreshold value; thereby forming a summary table; vii) displaying a TypeI report tabulating patient identifier, patient phenotype, laboratoryidentifier, and change % AUC for each victim in said summary table; and,viii) flagging the report as a service. 9: The method of claim 8,wherein said patient record comprises a plurality of patient phenotypes,and said operations comprise calculating a ΣCP score for each victim,where said ΣCP score is the sum of the CP scores over said plurality ofphenotypes, and computing change % AUC for each victim from the ΣCPscore in said summary table. 10: A business method for obtainingreimbursement for pharmacogenetic interpretive services, whichcomprises: a) implementing a billing server configured for detecting aflag associated with a service of claim 1; b) invoicing a payorassociated with the patient identifier. 11: The business method of claim10, wherein said payor is a third party payor and said billing servercomprises an insurance submodule. 12: A business method for obtainingreimbursement for pharmacogenetic interpretive services, whichcomprises: a) implementing a billing server configured for detecting aflag associated with a service of claim 8; b) invoicing a payorassociated with the patient identifier. 13: The business method of claim12, wherein said payor is a third party payor and said billing servercomprises an insurance submodule. 14: The business method, asimplemented on a computerized host system, for obtaining automatedthird-party reimbursement by providing pharmacogenetic interpretiveservices for preventing a possible adverse drug reaction, comprising thesteps of: a) providing a first user with a means for accessing a hostsystem and a means for entering a patient record comprising a patientidentifier of a patient and a genotype associated with said patientidentifier, and thereupon b) on command of said first user, translatingsaid genotype into a phenotype and entering said phenotype in saidpatient record; c) providing a second user with a means for entering aplurality of factors into the patient record, wherein the factors arefrom the group consisting of prescription drug(s) prescribed,substance(s) used, clinical factor(s); d) upon command of said seconduser, computing a change % AUC for any interacting pairs of factorsentered, computing a prediction warning of a potentialbioincompatibility between said interacting pairs, wherein saidprediction is made by a PK predictive algorithm of claim 1, anddisplaying a report; and, e) flagging the prediction as a billableservice; and wherein the method is further characterized in thatreimbursement is made according to a prearranged fee schedule between anoperator of the host system and a third party payor contracted by saidpatient to pay for said billable service.
 15. The business method, asimplemented on a computerized host system, for obtaining automatedthird-party reimbursement by providing pharmacogenetic interpretiveservices for preventing a possible adverse drug reaction, comprising thesteps of: a) providing a first user with a means for accessing a hostsystem and a means for entering a patient record comprising a patientidentifier of a patient and a genotype associated with said patientidentifier; and thereupon b) on command of said first user, translatingsaid genotype into a phenotype and entering said phenotype in saidpatient record; c) upon command of said first user, selecting a list ofdrugs, computing a change % AUC for any interacting pairs of factorsentered, computing a prediction warning of a potential interactionbetween said drug and phenotype, wherein said prediction is made by a PKpredictive algorithm of claim 8; and preparing a Type I lab reportcomprising a pharmacogenetic interpretive service; d) upon command ofsaid first user; transmitting said report to a customer, wherein saidcustomer is a customer of said first user and flagging said transactionto a billing server operated by said first user; e) receiving areimbursement from said first user for access to said host system;wherein the method is further characterized in that the billing serveroperated by said first user automatically bills for said pharmacogeneticinterpretive service. 16: The business method of claim 15, furthercharacterized in that said billing server automatically bills a thirdparty payor contracted by said patient to pay for said pharmacogeneticinterpretive service. 17: A business apparatus for obtaining automatedreimbursement for pharmacogenetic interpretive services, said apparatuscomprising a computerized host system operated by a host system operatorand having a means for data storage, a means for data processing, ameans for networking, a first graphical user interface for access to thehost system by a first user, a second graphical interface for access tothe host system by a second user, wherein said apparatus is configuredwith means for: a) under control of said first user, entering andstoring a laboratory identifier, patient identifier and a genetic testresult comprising a patient genotype in a patient record on said firstgraphical user interface of said host system, said first user being alaboratory with a client relationship with said host system operator; b)on command of said first user, performing a phenotypic interpretation ofsaid genotype entering said phenotype in said patient record on saidhost system; c) on command of said first user, using a first predictivealgorithm resident in said host system to prepare a predictive drug-geneinteraction report (a Type 1 report); d) under control of said hostsystem operator, appending a hyperlink to said predictive drug-geneinteraction report, said hyperlink having the property of linking tosaid second graphical user interface; e) on command of said first user,transmitting said predictive drug-gene interaction report with appendedhyperlink to said second user, said second user being a patient or aresponsible medical care provider; f) under control of said second user,opening said second graphical user interface when said second useraccesses said appended hyperlink; g) under control of said second user,editing said patient record to add a list of factors to be associatedwith said patient identifier, wherein said factors are selected from thegroup consisting of prescription drug, substance, and clinical factor;h) on command of said second user, using a second predictive algorithmresident in said host system to prepare a predictive drug-drug anddrug-gene interactive report (a Type II report), flagging saidpredictive drug-drug and drug-gene interactive report as apharmacogenetic interpretive service, and displaying said Type II reportto said second user on said second graphical interface; i) in responseto said flag, billing said second user for said pharmacogeneticinterpretive service, wherein said second user has preselected a paymentmethod by entering a financial instrument at a paypoint associated withsaid second graphical user interface. 18: The business apparatus ofclaim 17, wherein said paypoint is configured for entering a financialinstrument to be used as payment for said pharmacogenetic interpretiveservice. 19: The business apparatus of claim 18, wherein said financialinstrument is selected from insurance information, credit cardinformation, on-line debit information, sponsored use access code, trialuse access code, or subscription information. 20: The business apparatusof claim 19 wherein said paypoint is configured with a menu for choosinga method of payment selected from a) free trial period withauthorization code, b) credit card payment for access, c) debit cardinformation; d) entry of insurance information and on-line authorizationfrom the insurer, e) subscription payment for access, f) sponsored usewith authorization code, and g) wholesale group contract payment foraccess.